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Wavelet transform

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A discussion of wavelet ecompositions inthe context of Littlewood-Paley theory can be found in the monograph of Frazier,Jawerth, and Weiss [FJW]. We shall also not attempt to give a complete discussionof the history of wavelets.

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WAVELETS
RONALD A. DeVORE and BRADLEY J. LUCIER1. Introduction
The subject of “wavelets” is expanding at such a tremendous rate that it isimpossible to give, within these few pages, a complete introduction to all aspects of its theory. We hope, however, to allow the reader to become suﬃciently acquaintedwith the subject to understand, in part, the enthusiasm of its proponents towardits potential application to various numerical problems. Furthermore, we hope thatour exposition can guide the reader who wishes to make more serious excursions intothe subject. Our viewpoint is biased by our experience in approximation theory anddata compression; we warn the reader that there are other viewpoints that are eithernot represented here or discussed only brieﬂy. For example, orthogonal waveletswere developed primarily in the context of signal processing, an application whichwe touch on only indirectly. However, there are several good expositions (e.g.,[Da1] and [RV]) of this application. A discussion of wavelet decompositions inthe context of Littlewood-Paley theory can be found in the monograph of Frazier,Jawerth, and Weiss [FJW]. We shall also not attempt to give a complete discussionof the history of wavelets. Historical accounts can be found in the book of Meyer[Me] and the introduction of the article of Daubechies [Da1]. We shall try to giveenough historical commentary in the course of our presentation to provide somefeeling for the subject’s development.The term “wavelet” (srcinally called wavelet of constant shape) was introducedby J. Morlet. It denotes a univariate function
ψ
(multivariate wavelets exist as welland will be discussed subsequently), deﬁned on
R
, which, when subjected to thefundamental operations of shifts (i.e., translation by integers) and dyadic dilation,yields an orthogonal basis of
L
2
(
R
). That is, the functions
ψ
j,k
:= 2
k/
2
ψ
(2
k
·−
j
),
j,k
∈
Z
, form a complete orthonormal system for
L
2
(
R
). In this work, weshall call such a function an orthogonal wavelet, since there are many general-izations of wavelets that drop the requirement of orthogonality. The Haar function
H
:=
χ
[0
,
1
/
2)
−
χ
[1
/
2
,
1)
, which will be discussed in more detail in the section thatfollows, is the simplest example of an orthogonal wavelet. Orthogonal wavelets withhigher smoothness (and even compact support) can also be constructed. But before
A version of this paper appeared in
Acta Numerica,
A. Iserles, ed., Cambridge UniversityPress, v. 1 (1992), pp. 1–56. This work was supported in part by the National Science Foundation(grants DMS-8922154 and DMS-9006219), the Air Force Oﬃce of Scientiﬁc Research (contract89-0455-DEF), the Oﬃce of Naval Research (contracts N00014-90-1343, N00014-91-J-1152, andN00014-91-J-1076), the Defense Advanced Research Projects Agency (AFOSR contract 90-0323),and the Army High Performance Computing Research Center at the University of Minnesota.1
2 RONALD A. DEVORE AND BRADLEY J. LUCIER
−
1 0 1
φ j
−
12
k
j
2
k
j
+ 12
k
φ
(2
k
·−
j
)
Figure 1.
An example of functions
φ
and
φ
(2
k
·−
j
).considering that and other questions, we wish ﬁrst to motivate the desire for suchwavelets.We view a wavelet
ψ
as a “bump” (and think of it as having compact support,though it need not). Dilation squeezes or expands the bump and translation shiftsit (see Figure 1). Thus,
ψ
j,k
is a scaled version of
ψ
centered at the dyadic integer
j
2
−
k
. If
k
is large positive, then
ψ
j,k
is a bump with small support; if
k
is largenegative, the support of
ψ
j,k
is large.The requirement that the set
{
ψ
j,k
}
j,k
∈
Z
forms an orthonormal system meansthat any function
f
∈
L
2
(
R
) can be represented as a series(1.1)
f
=

j,k
∈
Z

f,ψ
j,k

ψ
j,k
with

f,g

:=

R
fgdx
the usual inner product of two
L
2
(
R
) functions. We view(1.1) as building up the function
f
from the bumps
ψ
j,k
. Bumps corresponding tosmall values of
k
contribute to the broad resolution of
f
; those corresponding tolarge values of
k
give ﬁner detail.The decomposition (1.1) is analogous to the Fourier decomposition of a function
f
∈
L
2
(
T
) in terms of the exponential functions
e
k
:=
e
ik
·
, but there are importantdiﬀerences. The exponential functions
e
k
have global support. Thus, all termsin the Fourier decomposition contribute to the value of
f
at a point
x
. On theother hand, wavelets are usually either of compact support or fall oﬀ exponentiallyat inﬁnity. Thus, only the terms in (1.1) corresponding to
ψ
j,k
with
j
2
−
k
near
x
make a large contribution at
x
. The representation (1.1) is in this sense local. Of course, exponential functions have other important properties; for example, theyare eigenfunctions for diﬀerentiation. Many wavelets have a corresponding propertycaptured in the “reﬁnement equation” for the function
φ
from which the wavelet
ψ
is derived, as discussed in
§
3.1.Another important property of wavelet decompositions not present directly inthe Fourier decomposition is that the coeﬃcients in wavelet decompositions usuallyencode all information needed to tell whether
f
is in a smoothness space, such as
WAVELETS 3
the Sobolev and Besov spaces. For example, if
ψ
is smooth enough, then a function
f
is in the Lipschitz space Lip(
α,L
∞
(
R
)), 0
< α <
1, if and only if (1.2) sup
j,k
2
k
(
α
+
12
)
|
f,ψ
j,k
|
is ﬁnite, and (1.2) is an equivalent seminorm for Lip(
α,L
∞
(
R
)).All this would be of little more than theoretical interest if it were not for thefact that one can eﬃciently compute wavelet coeﬃcients and reconstruct functionsfrom these coeﬃcients. Such algorithms, known as “fast wavelet transforms” arethe analogue of the Fast Fourier Transform and follow simply from the reﬁnementequation mentioned above.In many numerical applications, the orthogonality of the translated dilates
ψ
j,k
is not vital. There are many variants of wavelets, such as the prewavelets proposedby Battle [Ba] and the
φ
-transform of Frazier and Jawerth [FJ], that do not requireorthogonality. Typically, for a given function
ψ
, one wants the translated dilates
ψ
j,k
,
j,k
∈
Z
, to form a stable basis (also called a Riesz basis) for
L
2
(
R
). Thismeans that each
f
∈
L
2
(
R
) has a unique series decomposition in terms of the
ψ
j,k
,and that the

2
norm of the coeﬃcients in this series is equivalent to

f

L
2
(
R
)
(thiswill be discussed in more detail in
§
3.1). In other applications, when approximatingin
L
1
(
R
), for example, one must abandon the requirement that
ψ
j,k
,
j,k
∈
Z
, forma stable basis of
L
1
(
R
), because none exists. (The Haar system is a Schauder basisfor
L
1
([0
,
1]), for example, but the representation is not
L
1
([0
,
1])-stable.) For suchapplications, one can use redundant representations of
f
, with
ψ
a box spline, forexample.We have, to this point, restricted our discussion to univariate wavelets. Thereare several constructions of multivariate wavelets but the ﬁnal form of this theoryis yet to be decided. We shall discuss two methods for constructing multivariatewavelets; one is based on tensor products while the other is truly multivariate.The plan of the paper is as follows. Section 2 is meant to introduce the topicof wavelets by studying the simplest orthogonal wavelets, which are the Haar func-tions. We discuss the decomposition of
L
p
(
R
) using the Haar expansion, the char-acterization of certain smoothness spaces in terms of the coeﬃcients in the Haarexpansion, the fast Haar transform, and multivariate Haar functions. Section 3concerns itself with the construction of wavelets. It begins with a discussion of the properties of shift-invariant spaces, and then gives an overview of the construc-tion of univariate wavelets and prewavelets within the framework of multiresolution.Later, mention is made of Daubechies’ speciﬁc construction of orthonormal waveletsof compact support. We ﬁnish with a discussion of wavelets in several dimensions.Section 4 examines how to calculate the coeﬃcients of wavelet expansions viathe so-called Fast Wavelet Transform. Section 5 is concerned with the characteri-zation of functions in certain smoothness classes called Besov spaces in terms of thesize of wavelet coeﬃcients. Section 6 turns to numerical applications. We brieﬂymention some uses of wavelets in nonlinear approximation, data compression (and,more speciﬁcally, image compression), and numerical methods for partial diﬀeren-tial equations.
4 RONALD A. DEVORE AND BRADLEY J. LUCIER
2. The Haar Wavelets
2.1. Overview.
The Haar functions are the most elementary wavelets. Whilethey have many drawbacks, chieﬂy their lack of smoothness, they still illustrate inthe most direct way some of the main features of wavelet decompositions. For thisreason, we shall consider in some detail the properties that make them suitable fornumerical applications. We hope that the detail we provide at this stage will rendermore convincing some of the later statements we make, without proof, about moregeneral wavelets.We consider ﬁrst the univariate case. Let
H
:=
χ
[0
,
1
/
2)
−
χ
[1
/
2
,
1)
be the Haarfunction that takes the value 1 on the left half of [0
,
1] and the value
−
1 on theright half. By translation and dilation, we form the functions(2.1.1)
H
j,k
:= 2
k/
2
H
(2
k
·−
j
)
, j,k
∈
Z
.
Then,
H
j,k
is supported on the dyadic interval
I
j,k
:= [
j
2
−
k
,
(
j
+ 1)2
−
k
).It is easy to see that these functions form an orthonormal system. In fact,given two of these functions
H
j,k
,
H
j

,k

,
k

≥
k
and (
j,k
)

= (
j

,k

), we havetwo possibilities. The ﬁrst is that the dyadic intervals
I
j,k
and
I
j

,k

are disjoint,in which case

R
H
j,k
H
j

,k

= 0 (because the integrand is identically zero). Thesecond possibility is that
k

> k
and
I
j

,k

is contained in one of the halves
J
of
I
j,k
.In this case
H
j,k
is constant on
J
while
H
j

,k

takes the values
±
1 equally often onits support. Hence, again

R
H
j,k
H
j

,k

= 0.We want next to show that
{
H
j,k
|
j,k
∈
Z
}
is complete in
L
2
(
R
). The followingdevelopment gives us a chance to introduce the concept of multiresolution, whichis the main vehicle for constructing wavelets and which will be discussed in moredetail in the section that follows. Let
S
:=
S
0
denote the subspace of
L
2
(
R
) thatconsists of all piecewise-constant functions with integer breakpoints; i.e., functionsin
S
are constant on each interval [
j,j
+1),
j
∈
Z
. Then
S
is a
shift-invariant space
:if
S
∈ S
, each of its
shifts,
S
(
·
+
k
),
k
∈
Z
, is also in
S
. A simple orthonormalbasis for
S
is given by the shifts of the function
φ
:=
χ
[0
,
1]
. Namely, each
S
∈ S
has a unique representation(2.1.2)
S
=

j
∈
Z
c
(
j
)
φ
(
· −
j
)
,
(
c
(
j
))
∈

2
(
Z
)
.
By dilation, we can form a
scale
of spaces
S
k
:=
{
S
(2
k
·
)
|
S
∈S}
, k
∈
Z
.
Thus,
S
k
is the space of piecewise-constant
L
2
(
R
) functions with breakpoints atthe dyadic integers
j
2
−
k
. The normalized dyadic shifts
φ
j,k
:= 2
k/
2
φ
(2
k
·−
j
) =2
k/
2
φ
(2
k
(
· −
j
2
−
k
)) with step
j
2
−
k
,
j
∈
Z
, of the function
φ
(2
k
·
), form an or-thonormal basis for
S
k
. However, to avoid possible confusion, we note that thetotality of all such functions
φ
j,k
is not a basis for the space
L
2
(
R
) because thereis redundancy. For example,
φ
= (
φ
0
,
1
+
φ
1
,
1
)
/
√
2.Clearly, we have
S
k
⊂ S
k
+1
,
k
∈
Z
, so the spaces
S
k
get “thicker” as
k
getslarger and “thinner” as
k
gets smaller. We are interested in the limiting spaces(2.1.3)
S
∞
:=

S
k
and
S
−∞
:=

S
k
,

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